Computers & Geosciences 27 (2001) 357–361

Short Note

MINFO } a prototype mineral information databasefor iron ore resources of India

Indranil Roy, B.C. Sarkara,*, A. Chattopadhyayb

aDepartment of Applied Geology, Indian School of Mines, Dhanbad - 826 004, Bihar, IndiabDepartment of Computer Science & Engineering, Indian School of Mines, Dhanbad - 826 004, Bihar, India

Received 20 May 1999; accepted 4 May 2000

1. Introduction

The ever-increasing demand for mineral resources has

created an equally large demand for informationpertaining to those resources. Exploration and asso-ciated mining activities generate enormous amounts of

information. In India, this information mostly remainsat its source due to the lack of communication andabsence of a central repository of such data. Theproblem can be solved by establishing a computerised

mineral information database.India is endowed with extensive iron-ore resources

distributed geographically in five major zones, viz. Zone

A (Southern Bihar and Northern Orissa), Zone B(Bastar, Rajhara, Rowghat areas of Madhya Pradesh),Zone C (Bellary, Hospet regions of Karnataka), Zone D

(Goa) and Zone E (Bababudan, Kudremukh area ofKarnataka) (Fig. 1). The resource position has increasedfrom 5000mt in 1955 to 12,000mt in 1993 (Banerjee andSharma, 1994). Presently, there are 259 working iron ore

mines and several hundreds of unexploited deposits existin India (Indian Bureau of Mines, 1992). With thisresource scenario at hand, and in accordance with the

new National Mineral Policy of India, an attempt hasbeen made to develop a mineral information database,MINFO, for iron-ore resources of India. To date, the

prototype system includes information on a total of 32iron-ore deposits from Zone A. Information on thosedeposits has been collected from various published

documents and individual mine reports. Tabulationsheets have been prepared for each deposit for informa-tion processing. To ensure reliability, visits to mineswere undertaken for cross-checking the information.

Next, the individual deposit data files have been createdand linked to the system.This paper is a description of the prototype mineral

information database, MINFO, developed for iron-oreresources of India. The main purpose of the paper is tohighlight the architecture and information management

system of the MINFO database, including the structureof the data files and user interface.

2. System requirements

The MINFO database has been developed using

TURBO PASCAL (Ver. 6.0) and operates under a DOS3.0 or higher platform on any IBM PC compatiblecomputer, preferably 486 or higher microprocessor, with

at least 640 kB RAM. The hard disk space requirementis 2.46MB for the core module. The storage spacerequirement for the database part grows with the

database size with 1.84 kB for each deposit data.

3. Variables managed by the database

To define a deposit adequately, seven broad categories

of information have been considered, in accordance withClark and Cook (1978). These include (i) informationfor cataloguing and overall management, (ii) informa-tion regarding the geographic location of a deposit,

(iii) information on lease holding and other legal aspects,(iv) descriptions of existing mineralogy, ore types andbeneficiation factors, (v) information on various cate-

gory of reserves, (vi) information pertaining to depositgeology, and (vii) information related to current miningpractices. In the MINFO mineral information database,

these seven main categories of information have beenfurther partitioned into a total of 64 subheads termed as

*Corresponding author. Tel.: +91-326-202486; fax: +91-

326-206319.

E-mail address: bhabesh_sarkar@hotmail.com

(B.C. Sarkar).

0098-3004/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved.

PII: S 0 0 9 8 - 3 0 0 4 ( 0 0 ) 0 0 1 0 1 - 1

fields. The various information categories and relatedfields are shown in Table 1.

4. Database architecture and files

The MINFO mineral information database is com-posed of a core information management program filenamed MINFO.EXE, three system files (CONFIG.MNF, FIELDS.MNF and HELP.MNF) and two types

of data files, viz. master record file and individualdeposit information files. Architecturally, the systemfiles and the master record file (the main data file) are

linked to the core program file. On the other hand, theindividual deposit information data files are linked tothe master record file in a hybrid structure. The database

architecture is shown in Fig. 2.The master record portion of the database, which acts

as a deposit catalogue, is tabular in nature. Each record

in this table represents a single deposit, and is linked to aseparate file that contains the information about thedeposit. Each of the deposit detail files is small (184 kB),enabling rapid access and limited memory usage for

optimal performance on a PC loaded with DOS.Records in the master table store the deposit name,detail record file name, deposit code, date of creation,

name of the creator, date of last modification, name ofthe modifier, and flags indicating the presence or absenceof any category of detailed information for the deposit.

The flags speed up the querying process. Addition ordeletion of any deposit is reflected automatically in the

master record. Hierarchically structured individualdeposit information files contain a header, consisting

of deposit name and unique deposit code, and fields fordescriptive deposit information categories as depicted inTable 1.The system file FIELDS.MNF stores a general list of

various data fields and their hierarchical relationships.This comes in handy for structuring user-definedqueries. In the query process, though the selection of

primary fields are predefined, virtually any combinationof different fields can be used to create the final searchargument. This allows for a greater flexibility in the

range of query construction. Using a user-defined query,the system searches the database in two stages. Initially,a list of deposit names is created by sequentiallysearching the master record file by testing the flags of

information categories, as required by the query, againstthe deposit names. Negative flags force the deposit nameto be excluded from the list. Then, using the master

record file as a node for multiple link (i.e. using the flagsas pointer to the files), all the individual data files for thedeposit names in the list are accessed and tested against

the query argument. A new list is then created to storethe results.

5. User Interface

User Interface of the MINFO database is a multi-level

menu based with few hot keys (that serve specialpurposes, as illustrated in Fig. 3). Of the various options

Fig. 1. Geographic distribution of major iron-ore zones of India.

I. Roy et al. / Computers & Geosciences 27 (2001) 357–361358

under main menu (Fig. 3), the View option allows theuser to browse the information stored in the database(Fig. 4). The Edit option is utilized for updating and

rectifying data-entry errors. The Add New and Add Dataoption are used to add new deposit records andinformation categories, respectively, to the database.

The process creates a new data file and inserts headerinformation for the deposit within the master record file.On the other hand, the Delete option is used to removethe record of a selected deposit from the database, which

involves physical removal of the data file from thepermanent storage as well as removal of its record from

the master record file. Using the Report option, thestored information can be printed or exported from thedatabase as ASCII text files.

The querying process of the MINFO mineral infor-mation database happens in two stages. The user firstformulates a query using a sequence of related menu

structure. Each of the search arguments are built with auser-specified numerical or string entity, joined by alogical operator (=, 5, �, >, � and 6¼) to a particulardata field. These search arguments can further be joined

with each other by Boolean operators (AND, OR andNOT) (Fig. 5). The continuous system response (by

Table 1

Different information categories and related fields

Information category Fields FTa FWb Information category Fields FTa FWb

Record Deposit name S 20 Current reserve

identification Record file name S 20 Proved N 6

Unique individual code N 2 Probable N 6

Possible N 6

Legal Owner S 25

information Address S 25 Geology and Stratigraphic position S 45

Mining start N 6 deposit Age S 20

Lease area N 6 information Host rock S 20

Lease start N 6 Regional structure S 50

Lease end N 6 Local structure S 50

Annual production N 6 Dip of the orebody N 2

Production till date N 6 Strike of the orebody N 2

Thickness of orebody N 6

Location Latitude N 8 Ore controls S 50

information Longitude N 8 Alterations S 50

Altitude N 6 Topography S 15

Toposheet No. S 20 Drainage pattern S 20

District S 20 Overburden type S 12

State S 20 Depth of overburden N 6

Country S 20 Depth of basement N 6

Commodity Major elements S 10 Mining Mine type S 20

information Minor elements S 10 information Cut off grade N 6

Other elements S 30 Number of drill holes N 2

Major ore minerals S 15 Total drilled length N 6

Minor ore minerals S 15 Drill pattern S 20

Other ore minerals S 30 Drill spacing (X) N 6

Ore types S 150 Drill spacing (Y) N 6

Comments S 150 B/L logical variable L 1

Number of bench N 2

Reserves and Geological reserve Bench height N 6

resources Proved N 6 Working bench number S 20

information Probable N 6 Number of levels N 2

Possible N 6 Level separation N 6

Mining reserve Working level number S 20

Proved N 6 Daily production N 6

Probable N 6

aFT: Field type (N: numeric; S: string; L: logical).bFW: Field width (number of characters in case of string, number of bytes in case of numeric field and number of bits in case of

logical field).

I. Roy et al. / Computers & Geosciences 27 (2001) 357–361 359

insertion of context-sensitive words within the query forthe completeness of the query as a normal Englishsentence) helps the user to build a query fairly close tothe natural language. The parsed query is then executed

and result is directed to the screen (default), printer orfile depending on the user’s discretion.

6. Conclusions

In view of extensive iron-ore resources of India, theMINFO database can provide a quick and pragmatic

means for storage and rapid search and retrieval of therequired information, ensuring the minimum redun-dancy. This will be helpful to disseminate well-organized

and specific information as and when required withan aim to describe and quantify resources forfuture resource assessment, planning and exploration

purposes, as well as for the policy formulation andmodification.The system is not complete by itself and further

modules on environmental parameters and mine infra-

structural facilities are currently being incorporated. Inanticipation of further development, the system archi-tecture has been kept sufficiently open for future

incorporation of other commodities. By utilizing theconfiguration option, the MINFO mineral informationdatabase engine can be customized to work with any

data set and hence, can lead towards development of amore comprehensive mineral commodity informationsystem.

Acknowledgements

The first author acknowledges the CSIR for financial

support through research Grant No. 9/85/(83)/96/EMR-1. The second and third authors acknowledge

Fig. 2. Architecture of MINFO mineral information database.

Fig. 3. User interface of MINFO database.

Fig. 4. Geological Information records of deposit under View

mode.

Fig. 5. Query formulation by logical combination of various

fields.

I. Roy et al. / Computers & Geosciences 27 (2001) 357–361360

the AICTE for financial support through researchproject Grant No. TMAT 020/REC 387. Thanks are

also due to the anonymous reviewers for providingconstructive suggestions.

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Indian Bureau of Mines, 1992. Growth of Indian Mineral

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